Earthquake Early Warning: History
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Contributor:
Paola Pierleoni
,
Roberto Concetti
,
Alberto Belli
,
Lorenzo Palma
,
Simone Marzorati
,
Marco Esposito

Earthquake early warning (EEW) systems have been rapidly developing as a result of the development of information technology. EEW systems are able to analyze ground motions in real time and provide alerts before the onset of the destructive seismic waves at specific targets. EEW systems can be divided into on-site, regional, and hybrid EEW systems. In regional EEW systems, a sequence of streams from different stations  are analyzed at a central hub through a “picking” algorithm that is able to detect P-wave onsets in the seismic traces of multiple stations, and then use this information for earthquake localization and event declaration. On the contrary, an on-site EEW system is based on sensors installed near a target to monitor, where an intensity estimation is produced based on the initial P-wave detection at the target. Lastly, these two approaches are combined in a hybrid EEW system. Early alarm dissemination and source localization through EEW systems might allow the end-users of these system to take precautionary measures, aid disaster responders, and automate safety measures in certain scenarios, with important benefits in terms of risk reduction, first response, and disaster management.

  • earthquake early warning (EEW)
  • cloud computing
  • earthquake localization
  • early warning system
  • internet of things

1. Introduction

Earthquake early warning (EEW) systems have been rapidly developing in recent years as a result of the development of information technology. EEW systems are able to analyze ground motions in real time and provide alerts before the onset of the destructive seismic waves at specific targets [1]. An effective earthquake early warning system requires therefore rapid and reliable earthquake source detection. These systems are mainly based on the different propagation speeds of the waves generated by an earthquake. When a seismic event occurs, different types of seismic waves propagate from the hypocenter, namely Primary waves (P-waves), Secondary waves (S-waves), and surface waves. The greatest destructive power is contained in the S-waves and in the surface waves. P-waves contain relevant information about the event in progress and do not carry the same destructive power, while also moving at higher speeds. An EEW system must be able to identify the arrival of P-waves and send alerts in case of a potential harmful event. Another important feature of an EEW system is the ability to estimate the position of the earthquake source and the arrival times at a certain target location. The hypocenter and/or epicenter localization problem can be solved by looking at the arrival times, incidence direction, and/or horizontal velocity of seismic waves at a seismic station, using a single-station approach or utilizing multiple stations [2]. In regional EEW systems, a sequence of streams from different stations at a central server or a data center are analyzed at a central hub through a “picking” algorithm that is able to detect P-wave onsets in the seismic trace, also called “picks” in seismological jargon. The detected P-wave picks are then used to estimate the earthquake source. There are different methods that are able to give an estimation of the epicenter location, and the estimation error is usually a function of the number of triggers [3]. Since stations are located at different distances from the epicenter, this also means that the estimation error becomes a function of time. Therefore, a greater concentration of stations in the area for the real-time acquisition of waveforms leads to benefits both in terms of the correctness of results and detection times. Until a few years ago, the densification of seismic networks would have been challenging due to the high costs of dedicated seismic instrumentation. However, recent developments in the Internet of Things (IoT) have paved the way for promising low-cost solutions in a growing number of applications, such as smart homes [4][5][6], smart cities [7][8], renewable resources systems [9][10], and disaster management [11][12]. The IoT represents therefore a promising tool for the densification of earthquake monitoring systems, which is an essential feature for quick and precise earthquake detection and localization. Furthermore, the IoT enables the smart management of earthquake data through the integration of dedicated Cloud resources in the IoT architecture, and quick dissemination of early warning.

2. Earthquake Early Warning and Localization

EEW systems can be divided into on-site, regional, and hybrid EEW systems [13][14]. A regional EEW system is used to detect, locate, and determine the magnitude of seismic events analyzing streams from stations belonging to a regional monitoring network [14]. Notable examples of regional EEW systems are ShakeAlert [15], PRESTo [16], and the Virtual Seismologist [17]. PRESTo [18] is a software platform for regional EEW that contains modules for waveform data acquisition from stations using the Seedlink protocol, seismic phase picking using the FilterPicker algorithm, source localization, and peak magnitude estimation [16].
Studies have shown that implementing a PRESTo in Italy could theoretically deliver alert messages within approximately 5 to 10 s, with maximum lead times for implementing security measures at a distant target of about 25 s [13]. This system, therefore, represents the current state-of-the-art in regional EEW technology in the country and is one of the most used software solutions for EEW in Europe [19]. Besides PRESTo, the other most widely used regional EEW software in Europe is VS(SC) (Virtual Seismologist in SeisComP) [17]. VS(SC) incorporates phase picking using the Short-Time Average over Long-Time Average (STA/LTA) algorithm [20], and location and magnitude estimation algorithms integrated in the Seiscomp software module [21]. It has been shown that PRESTo is ultimately the best choice for regional EEW compared to the VS(SC) module [19], as it provides better short-term predictions, a key requirement for EEW. Furthermore, PRESTo has undergone extensive implementation and testing in multiple regions and scenarios, including Southern Italy, Turkey, Romania, Southern Iberia, Austria, and Slovenia [19]. Additionally, the Italian national earthquake monitoring agency (Istituto Nazionale di Geofisica e Vulcanologia, INGV) has conducted tests to assess its performance when integrated with existing monitoring infrastructures across different regions.
In recent years, there have been many advances in the use of the IoT for early warning purposes, including EEW [11], taking advantage of the developments in Micro-Electro-Mechanical System (MEMS) technology, cloud and edge computing, and the crowd-sensing paradigm. A notable example of a mobile crowd-sensing application for EEW is the MyShake application [22], which distributes computing among smartphone devices. Each device running the MyShake app is used for on-site earthquake detection using data from the smartphone-embedded sensors, and then a centralized algorithm is used to distinguish between “true” earthquake events and false triggers. Two notable works that heavily integrate cloud computing in their architectures are the SeismoCloud project [23] and the EarthCloud project [24]. The former is another example of smartphones used to detect seismic events, even though any ground motion measuring device can be integrated in the developed cloud platform, while the latter makes use of IoT devices equipped with geophones that stream data towards an Amazon Web Services (AWS) IoT Core. A warning is issued when a certain number of devices from the same location exceed a set threshold. Similar to this work, the use of cloud computing provides these solutions with enhanced computational capabilities, but also additional services that can be used for device monitoring, reliable data dispatching, data storage, etc.
There are different computer-based approaches for automated earthquake locations that use seismic-phase arrival time observations. Digital computers permit the use of iterative linearized approaches based on Geiger’s method [25], while the increasing power of computing has made possible the use of large-scale, grid and stochastic direct searches with probabilistic nonlinear methods that provide a complete description of location uncertainties [26]. A far simpler manual method for epicenter localization consists in drawing a circle around the stations that were triggered by the earthquake with a radius equal to the epicentral distance, calculated from the P-wave and S-wave arrival times and assuming a constant speed for both waves. The epicenter then lies in a locus given by the intersection of the circles, and it can be roughly determined choosing, for example, the center of gravity of the locus. Graphical methods such as the hyperbola method have become, once again, interesting with the development of EEW systems that require speed, simplicity, and reliability [27]. PRESTo uses the RTLoc algorithm, a derivative of the hyperbola method [3]. Another technique based on arrival times and the hyperbola method is the one developed by Rydelek et al. [28]. This method only requires a subarray of two stations to detect the earthquake source and it is based on the concept of not-yet-arrived data. It is a quick method whose effective time savings depend on the station geometry and the epicentral position. The looser focus on precision is justified by the EEW application scenario. EEW systems are usually employed to detect strong seismic events that might have large fault ruptures, and with the more pressing requirement of acting quickly rather than with high accuracy.
Recently, Machine Learning (ML)-based approaches to localization have also seen a wider use. While some are based on waveforms detected at different stations or even single stations [29][30], others make use of the wave arrival times to estimate the source of the earthquake in a similar way to some of the previously described methods. Convolutional neural networks [31], attention-based networks [32], and other ML methods [33] have shown good accuracy in determining the epicentral/hypocentral position, with different degrees of spatial resolution; however, to the authors’ knowledge, most have either been tested in a vacuum or do not provide figures on their time performance, with the exception of [33], which provides some estimated figures on its implementation in an EW scenario.
Studies have demonstrated the feasibility of utilizing TDoA for earthquake localization [34][35][36]. However, these works do not evaluate the application of their method in the EEW scenario and focus on enhancing focal parameter estimation compared to state-of-the-art estimation [34], or, more generally, adopt complex 3D models [36], prioritizing accuracy and/or robustness over timeliness.
As accuracy is not a main constraint in EEW, accurate estimations of other parameters for describing a seismic event, such as accurate focal parameters, can be performed after the first, early alert.

This entry is adapted from the peer-reviewed paper 10.3390/s23208431

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